At present, there exist two main directions in data compression, i.e. lossless compression and lossy compression. In the former case, the restored signal matches the input one precisely, yet its shortcoming is that the compression ratio is rather low and does not generally exceed 2-3. In the latter case, much higher compression ratios may be achieved owing to the fact that the restored signal is nothing but an approximation to the original signal. In lossy compression systems, the required compression ratio may be set in a wide range, yet its shortcoming is that the restored signal distortion level would grow along with increasing the compression ratio. One of the known methods of lossy image compression, known as SIF compression and described in the above-referenced U.S. Pat. No. 8,374,446, effects compression by using a structure comprised of hierarchical pyramids (Laplacian pyramid). FIG. 1 presents a method of forming a Laplacian pyramid with three decimation levels. Low-pass filter (101) mitigates the aliasing effect on a reduced image. A traditional method of image compression by means of a Laplacian pyramid is successive interpolation of reduced copies of the original image down to the original size. In other words, (106) is used for obtaining (105), then (104), and finally (103). The difference between the interpolated image and the original is quantized and recorded in a file, or transmitted via a communication channel to be subsequently restored.
With such an approach, the original image compression efficiency is directly determined by quality of interpolated filters applied, as well as by quality of low-pass filters used prior to the decimation procedure in forming a Laplacian pyramid, as aliasing would lower the quality of operation of interpolators. To raise the compression efficiency, there have been suggested adaptive interpolation methods, as well as combining interpolation with two-dimensional adaptive prediction of values of the image elements.